Topic kernelization for real-time conversation data

ABSTRACT

Embodiments for text segmentation for topic modelling by a processor. Real-time conversation data may be analyzed and time intervals (e.g., inter-arrival times) between messages of the conversation data may be recorded. Each of the messages may be defined (and/or segmented) as burst segments or reflection segments according to the analyzing and recording. One or more topic modelling operations may be enhanced for text segmentation using the burst segments or reflection segments.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to computing systems, and moreparticularly to, various embodiments for topic kernelization forreal-time conversation data using a computing processor.

Description of the Related Art

Due to the recent advancement of information technology and the growingpopularity of the Internet, a vast amount of information is nowavailable in digital form. Such availability of information has providedmany opportunities. Digital and online information such as, for example,communication messaging in real-time has become very popular in recentyears.

SUMMARY OF THE INVENTION

Various embodiments for text segmentation for topic modelling by aprocessor are provided. Real-time conversation data may be analyzed andtime intervals (e.g., inter-arrival times) between messages of theconversation data may be recorded. Each of the messages may beclassified into a first group or a second group according to theanalyzing. One or more topic modelling operations may be enhanced fortext segmentation using the first group and the second group.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a block diagram depicting an exemplary cloud computing nodeaccording to an embodiment of the present invention;

FIG. 2 is an additional block diagram depicting an exemplary cloudcomputing environment according to an embodiment of the presentinvention;

FIG. 3 is an additional block diagram depicting abstraction model layersaccording to an embodiment of the present invention;

FIG. 4 is an additional diagram depicting analyzing real-timeconversation data and recording inter-arrival times between messages inaccordance with aspects of the present invention;

FIG. 5 is a diagram depicting messages grouped into two groups uponanalyzing the real-time conversation data from FIG. 4 in accordance withaspects of the present invention;

FIG. 6 is a diagram depicting a summary of text mining analysis inaccordance with aspects of the present invention;

FIG. 7 is a diagram depicting pseudocode results using topic modellingoperations in accordance with aspects of the present invention;

FIG. 8 is a diagram depicting an output of topic modelling of aconversation using enhanced topic modelling operations in accordancewith aspects of the present invention;

FIG. 9 is a flowchart diagram depicting an exemplary method for textsegmentation for topic modelling by a processor; again, in which aspectsof the present invention may be realized; and

FIG. 10 is a flowchart diagram depicting an exemplary method for topickernelization for real-time conversation data by a processor; again, inwhich aspects of the present invention may be realized.

DETAILED DESCRIPTION OF THE DRAWINGS

As the amount of electronic information continues to increase, thedemand for sophisticated information access systems also grows. Digitalor “online” data has become increasingly accessible through real-time,global computer networks. The data may reflect many aspects of variousorganizations and groups or individuals, including scientific,political, governmental, educational, businesses, and so forth. With theincreased use of collaborative and social communication, communicationvia text-based communication will also increase. For both business andrecreational purposes, real-time communication messages (e.g., real-timechat discourses) are part and parcel of modern society. However, forvarious entities, irrespective of size, using such collaborative andsocial means of communication can be an overwhelming experience,particularly when large volumes of text-based data are generated byvarious applications and services.

Moreover, various types of entities (e.g., businesses, organizations,governmental agencies, educational institutions, and the like) oftenengage in corpus linguistics, which is the study of language asexpressed in corpora (i.e., collections) of “actual use” text. The coreidea of corpus linguistics is that analysis of expression is bestconducted within its natural usage. By collecting samples of writing,researchers can understand how individuals converse with each other. Assuch, the present invention employs various techniques that assist inunderstanding and interpreting message based data.

In one aspect, topic modelling may be used to discover a semanticstructure within a text corpus. Topic modelling may employ one or moreoperations to infer the topic and meaning in text based documents and/ordiscourse. Topic modelling and text mining may be used to gain insightsinto the various communications. For example, if a business can minecustomer feedback on a particular product or service, this informationmay prove valuable. One of the recommendations when employing textmining/topic modelling techniques is that the more data available foranalysis, the better the overall results. However, even with the use oflarge data, practitioners may have a requirement to text mine a singleconversation or small text corpus to infer meaning.

In order for these text models to be successful, a number of steps areapplied to pre-process (“wash”) the text. These steps includetokenising, stemming, stop words removal, duplicate word removal andlemmatisation. It should be understood that as used herein (in relationto natural language processing “NLP”), the term tokenization may be theprocess of converting a sequence of characters (e.g., message discourse)into a series of tokens (e.g., strings with an assigned meaning).Therefore, before any analysis is conducted on a text corpus, the textmay be divided into linguistic elements such as, for example, words,punctuation, numbers and alpha-numerics. Stop words are words which arefiltered out before or after processing of text discourse. Stop wordstypically refer to the most common words used in a particular language.Stemming is a method of collapsing inflected words to the base or rootform of the word. For example, the words: “fishing,” “fished,” and“fisher,” could be reduced to the root word “fish.” The benefit ofstemming can be seen as follows: If a user is interested in termfrequency, it may be easiest to merely count the occurrence of the wordfish rather than the word's non-stemmed counterparts. Lemmatisation isthe process of grouping together the inflected words, for analysis as asingle entity. On the surface, lemmatisation may appear to be theopposite of stemming; however, the main difference is that stemming isunaware of the context of the words and thus, cannot differentiatebetween words that have other meanings depending on context. Forexample, the word “worse” has “bad” as its lemma. This link missed bystemming as a dictionary lookup is needed and the word “talk” is theroot of “talking.” This reference is matched in both stemming andlemmatisation.

However, the drawbacks of these cleaning techniques to pre-process(“wash”) the communication data is that once data is pre-processed thereare less words available for topic modelling. Conversely, if data werenot pre-processed an analysis may incur many issues in terms ofinferring topic models due to a lack of data cleansing (e.g., topterm=the). The problem is more acute for medium sized text corpora suchas real-time chat discourse conversations. For example, the belowanalysis depicted in Table 1 of three chat conversations shows thatafter the text was cleaned only 38% (on average) of words whereavailable for analysis.

TABLE 1 Conver- Conver- Conver- Metric sation 1 sation 2 sation 3 TotalWords 299 436 484 Non-Stopped Words 158 239 262 Unique Non-Stopped Words111 168 186 # words not analysed 188 268 298 % Potential words foranalysis 37% 39% 38%

Accordingly, various embodiments are provided herein to deliver a highdegree of summarization of real-time chat discourse. In one aspect, thepresent invention provides for topic mining operations by partitioningone or more conversations (e.g., real-time chat/messages) using asegmentation operation (e.g., grouping messaging into a burst group orreflective group) and to provide an increased number of words for topicsummarization tooling. Moreover, the topic modelling, using thesegmentation operation, may provide an increased number of words fortext mining but an improved level of readability rather than using anentire message corpus alone.

In one aspect, one or more topic modelling tools may also be employed.For example, Latent Semantic Analysis (“LSA”) may be used that allowsfor a low-dimension representation of documents and words. Byconstructing a document-term matrix, and using matrix algebra, documentsimilarity (e.g., product of row vectors) and word similarity (e.g.,product of column vectors) may be inferred. Another topic modelling toolthat may be employed is Latent Dirichlet allocation (“LDA”) which is agenerative statistical model that allows topics within a text corpus tobe represented as a collection of terms. At its core, LDA is athree-level hierarchical Bayesian model, in which each item in an arrayis modelled as a finite mixture over an underlying set of topics.

In one aspect, the present invention provides for topic kernelizationfor real-time conversation data using a computing processor. That is,the mechanisms of the illustrated embodiments provide for textsegmentation that provides more words for topic modelling and a moreprecise topic model. In one aspect, one or more real-time communications(e.g., real-time chat/messages) may be analyzed and the inter-arrivaltimes between each of the real-time chats/messages may be recorded. Theanalyzed real-time chat/messages may be grouped into two groups (burstsand reflections). That is, as the inter-arrival times of instant messageposts are determined and/or recorded, the messages may be grouped or“segmented” by, or according to, short and long inter-arrival times. Forexample, burst segments (e.g., burst segments or those messagescategorized as burst segments/messages) may be successive messageswithin a defined time period such as, for example, a zero minuteinter-arrival time (e.g., less than one minute inter-arrival time).Alternatively, reflective segments (e.g., reflective messages) may bethose messages having an inter-arrival time equal to or greater than adefined time period such as, for example, those messages with a oneminute or greater inter-arrival time.

In other words, one or more real-time communications (e.g., real-timechat/messages) may be analyzed. Those messages with a rapidinter-arrival time may be defined as a burst segment. Those messageswith a sedate inter-arrival time may be defined as a reflection segment.

In one aspect, topic kernelization is an operation to segmentunstructured communication message(s) (e.g., unstructured chatdiscourses) into a collection of active and passive messages. Forexample, burst segments (or those messages categorized as burstsegments) may be successive messages within a defined time period suchas, for example, a zero minute inter-arrival time (e.g., less than oneminute inter-arrival time). Alternatively, reflective messages may bethose messages having an inter-arrival time equal to or greater than adefined time period such as, for example, those messages with a oneminute or greater inter-arrival time.

The burst segments and reflection segments (e.g., collections of thegroup of burst segments and group of reflection segments) may then beused to determine optimal topic model sizes. The topic models may bedeveloped using the analysis to provide more precision when ran againstthe same or other corpus. The burst segments and reflection segments maybe used to increase words for analysis using a machine learningmechanism.

In an additional aspect, real-time conversation data may be analyzed andtime intervals between messages of the conversation data may berecorded. Each of the messages may be classified into a first group or asecond group according to the analyzing. One or more topic modellingoperations may be enhanced for text segmentation using the first groupand the second group.

Moreover, text mining may be performed on each burst and reflectionperiods (or groups) and the output terms of the text mining may then beaggregated. For topic text mining, one or more topic modellingoperations such as, for example, Biterm may be used. After aconversation has been a) segmented into burst and reflection periods, b)modelled (e.g., periods topic modelled) and c) the results aggregated,the efficacy of the output may be analyzed. The terms output from atopic model operation may not be explicitly designed for a readablesummary. Instead, the terms output from a topic model operation may bedesigned to provide a user an indication of the terms used in a textcorpus. However, since a user may desire to understand the output oftext mining, the present invention may prepare four sets of text asfollows. 1) Each conversation may be topic modelled such as, forexample, using Biterm (as a whole) and the mined terms may be outputinto a single collection. 2) The bursts (e.g., messages grouped asbursts) and reflections (e.g., messages grouped as reflections) fromeach conversation may be modelled individually, the terms are thenaggregated into a single collection. 3) Each conversation with one ormore stop words may be removed. 4) The raw conversation (i.e., withoutany pre-processing) may be modelled as well.

In an additional aspect, each of the text sets (e.g., the four text setsmentioned above) belonging to a single conversation may be summarized.Additionally, an analysis and/or feedback may be collected as to whichof the text sets are the easiest to summarize and/or which of the textsets (with all terms topic modelled) or two-grouped text sets (e.g.,bursts and reflections topic modelled) are most intuitive and mostefficient to summarize. Finally, feedback may be collected (which mayuse a machine learning algorithm) that describes or indicates (e.g.,from a user or application) which are the easiest and/or hardest textsets to summarize (as compared to each other).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,system memory 28 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in system memory 28 by way of example, and not limitation,as well as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

In the context of the present invention, and as one of skill in the artwill appreciate, various components depicted in FIG. 1 may be located ina moving vehicle. For example, some of the processing and data storagecapabilities associated with mechanisms of the illustrated embodimentsmay take place locally via local processing components, while the samecomponents are connected via a network to remotely located, distributedcomputing data processing and storage components to accomplish variouspurposes of the present invention. Again, as will be appreciated by oneof ordinary skill in the art, the present illustration is intended toconvey only a subset of what may be an entire connected network ofdistributed computing components that accomplish various inventiveaspects collectively.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Device layer 55 includes physical and/or virtual devices, embedded withand/or standalone electronics, sensors, actuators, and other objects toperform various tasks in a cloud computing environment 50. Each of thedevices in the device layer 55 incorporates networking capability toother functional abstraction layers such that information obtained fromthe devices may be provided thereto, and/or information from the otherabstraction layers may be provided to the devices. In one embodiment,the various devices inclusive of the device layer 55 may incorporate anetwork of entities collectively known as the “internet of things”(IoT). Such a network of entities allows for intercommunication,collection, and dissemination of data to accomplish a great variety ofpurposes, as one of ordinary skill in the art will appreciate.

Device layer 55 as shown includes sensor 52, actuator 53, “learning”thermostat 56 with integrated processing, sensor, and networkingelectronics, camera 57, controllable household outlet/receptacle 58, andcontrollable electrical switch 59 as shown. Other possible devices mayinclude, but are not limited to various additional sensor devices,networking devices, electronics devices (such as a remote controldevice), additional actuator devices, so called “smart” appliances suchas a refrigerator or washer/dryer, and a wide variety of other possibleinterconnected objects.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provides cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, in the context of the illustratedembodiments of the present invention, various workloads and functions 96for topic kernelization. In addition, workloads and functions 96 fortopic kernelization may include such operations as data analysis(including data collection and processing from organizational databases,online information, knowledge domains, data sources, and/or socialnetworks/media, and other data storage systems, and predictive and dataanalytics functions. One of ordinary skill in the art will appreciatethat the workloads and functions 96 for topic kernelization may alsowork in conjunction with other portions of the various abstractionslayers, such as those in hardware and software 60, virtualization 70,management 80, and other workloads 90 (such as data analytics and/orfungibility processing 94, for example) to accomplish the variouspurposes of the illustrated embodiments of the present invention.

As described herein, mechanisms of the illustrated embodiment provide asolution for text segmentation for topic modelling by a processor.Real-time conversation data may be analyzed and time intervals betweenmessages of the conversation data may be recorded. Each of the messagesmay be classified into a first group or a second group according to theanalyzing. One or more topic modelling operations may be enhanced fortext segmentation using the first group and the second group. After theconversation data has been segmented and grouped into either bursts orreflections, the bursts and reflections may be topic modelled. Theresults of topic modelling the bursts and reflections may then beaggregated. The terms output from a topic model operation is designed togive a user an indication of the terms used in a text corpus (and notnecessarily as a readable summary).

In one aspect, real-time conversation data may be analyzed and timeintervals between messages of the conversation data may be recorded.Each of the messages may be defined (and/or segmented) as burst segmentsor reflection segments according to the analyzing. One or more topicmodelling operations may be enhanced for text segmentation using theburst segments or reflection segments.

Turning now to FIG. 4, a block diagram 400 depicts analyzing real-timeconversation data and recording inter-arrival times. In one aspect, oneor more of the components, modules, services, applications, and/orfunctions described in FIGS. 1-3 may be used in FIGS. 4-8. For example,computer system/server 12 of FIG. 1, incorporating processing unit 16,may be used to perform various computational, data processing and otherfunctionality in accordance with various aspects of the presentinvention.

Also, as shown, the various blocks of functionality are depicted witharrows designating the blocks' 400 relationships with each other and toshow process flow. Additionally, descriptive information is also seenrelating each of the functional blocks 400. As will be seen, many of thefunctional blocks may also be considered “modules” of functionality.With the foregoing in mind, the module blocks 400 may also beincorporated into various hardware and software components of a systemfor topic kernelization in accordance with the present invention. Manyof the functional blocks 400 (such as, for example, those withincomputer system/server 12) may execute as background processes onvarious components, either in distributed computing components, or onthe user device, or elsewhere. In one aspect, the computer system 12(see FIG. 1) may be used (along with one or more other features,aspects, components, and/or hardware/software of FIGS. 2-3) for topickernelization.

At block 402, real-time conversation data (e.g., real-time chatmessages) (of one or more users or groups, entities, or otherparticipants in the real-time conversation of block 404) may beanalyzed. The time intervals between messages of the conversation datamay be determined and recorded, as in block 406. As indicated in block406, the recorded time intervals show recorded intervals that showconsistent time intervals, sporadic or lengthy time intervals, and/orrepeated “bursts” between messages. For example, in line one “hardshapers”, the recorded time intervals show recorded intervals where atime interval between the end of one message to the end of a secondmessage may be 0.67 milliseconds. In one aspect, hard shapers may referto a uniform distribution of conversation messages (e.g., chat messages)such as, for example, messages that have a constant inter-arrival timeover a collected time period. In one aspect, inter-arrival time may bethe time between the messages' (or objects) arrivals or the time betweenarrival of a message and a next message. In other words, theinter-arrival time may be defined as the amount of time between thearrival of one communication message (e.g., one real-time chat message)and the arrival of the next communication message (e.g., the nextreal-time chat message).

In line two “fluctuation”, the recorded time intervals show recordedintervals that are fluctuated and/or have inconsistent time intervalsbetween one message and the next message. In line three “burst”, therecorded time intervals show recorded intervals that are “bursts” wherea series of messages (after a first message and a time delay) arerepeated during a 10 ms time period.

Turning now to FIG. 5, diagram 500 depicts grouping the messages intotwo groups upon analyzing the real-time conversation data in FIG. 4.Accordingly, each of the recorded messages analyzed from FIG. 4 may begrouped into a first group (e.g., a burst) or a second group(reflections) according to the analyzing. As depicted, by way of exampleonly, a graph 502 depicts a time interval on the y-axis and the numberof words of each message on the x-axis. The highlighted sections of thegraph 502 are used for illustration purposes only for various messages.The messages may be grouped into the two groups 504 of burst segments orreflection segments.

For example, diagram 500 depicts those messages occurring during aselected time period (e.g., less than 15 seconds) as bursts such asburst 1, . . . , burst n, and those occurring outside of the selectedtime period (e.g., equal to or more than 15 seconds) are grouped in areflection group such as, for example, reflection 1, . . . , reflectionn, as shown in the two groups 504.

Turning now to FIG. 6, diagram 600 depicts a summary of text mininganalysis. After the conversation data has been segmented and groupedinto either bursts or reflections from FIGS. 4 and 5, the bursts andreflections may be topic modelled. The results of topic modelling thebursts and reflections may be aggregated. The data of each group ofbursts and reflections may be analyzed individually. For example, theremay be 10 bunches of data in the burst group and 5 in the reflectiongroup. For each bunch of the burst group and reflection group, a topicmodel such as, for example, LDA, Biterm, and the like may be used. Thatis, for each bunch of the burst group and reflection group, the presentinvention may select a topic cluster size, select a topic term size,determine (or calculate) a percentage (“%”) of unique stop words, and/oralter the topic cluster size and topic term size and then repeat untilthe percentage of unique stop words are maximized. The results, forexample, may be displayed in a summary such as, for example, summary600. In one aspect, the summary illustrates the number of clusters,cluster terms, total terms analyzed, unique term output, totalunreferenced terms, percentage of unique (or total) terms, percentage ofunique or non-stopped words, and percentage of unique words or stopwords, and/or a variety of other defined summary topics or terms. Thetopic model operations are developed and designed (using the group ofburst and group of reflection segments) to provide a user an indicationof the terms used in a text corpus (and not necessarily as a readablesummary).

FIG. 7 is a diagram 700 depicting exemplary pseudocode results usingtopic modelling operations. The depicted pseudocode 700 illustrates, byway of example only, the use of 10 bunches of data in the burst groupand 5 in the reflection group. The results of using the burst/reflectiongroups, to enhance topic modelling, in comparison to either segmentationbased on time or the whole corpus, results in a reduced set of duplicatewords within the resulting analysis.

As a final operation, FIG. 8 depicts an output of the entire analysisprocess described in FIGS. 4-8. For example, the resized topic models(from operation of FIG. 6) may be used by topic modelling operations(i.e., Biterm, LDA, and the like) to yield a higher precision of summarywhilst respecting context. As depicted in FIG. 8, diagram 800 depicts anoutput from topic modelling of a) an entire conversation and b)conversation modelled using bursts and reflections, as described herein.That is, diagram 800 depicts an analysis of the entire conversation, ananalysis of bursts and reflections, and an indication/results showing anincrease of at least fifteen to twenty percent more terms are availablefor analysis.

Turning now to FIG. 9, a method 900 for text segmentation for topicmodelling by a processor is depicted, in which various aspects of theillustrated embodiments may be implemented. That is, FIG. 9 is aflowchart of an additional example method 900 for topic kernelizationfor real-time conversation data in a computing environment according toan example of the present invention. The functionality 900 may beimplemented as a method executed as instructions on a machine, where theinstructions are included on at least one computer readable medium orone non-transitory machine-readable storage medium. The functionality900 may start in block 902. Real-time conversation data may be analyzedand time intervals between messages of the conversation data may berecorded, as in block 904. Each of the messages may be classified (orsegmented or defined) into a first group or a second group according tothe analyzing, as in block 906. In other words, the messages may bedefined as a burst segments or reflection segments. One or more topicmodelling operations may be enhanced (and/or developed) for textsegmentation using the first group and the second group, as in block908. The functionality 900 may end in block 910.

In one aspect, in conjunction with and/or as part of at least one blockof FIG. 9, the operation of 900 may include one or more of each of thefollowing. The operation of 900 may analyze the first group and thesecond group. The first group may be a burst group of occurring messageswithin a selected time period and the second group may be a reflectiongroup of occurring messages outside of a selected time period. The firstgroup and the second group may be used to determine optimal topic modelsizes. The operation of 900 may use the one or more enhanced topicmodels (e.g., a topic model operation using the first group and thesecond group) for selecting a topic cluster size, selecting a topic termsize, determining a percentage of unique stop words, and/or altering atopic cluster size and a topic term size until a percentage of uniquestop words are maximized.

Turning now to FIG. 10, a method 1000 for text kernelization by aprocessor is depicted, in which various aspects of the illustratedembodiments may be implemented. That is, FIG. 10 is a flowchart of anadditional example method 1000 for topic kernelization for real-timeconversation data in a computing environment according to an example ofthe present invention. The functionality 1000 may be implemented as amethod executed as instructions on a machine, where the instructions areincluded on at least one computer readable medium or one non-transitorymachine-readable storage medium. The functionality 1000 may start inblock 1002. Conversation data (e.g., real-time chat messages) may beanalyzed, as in block 1004. Time intervals (e.g., inter-arrival times)between messages of the conversation data may be recorded, as in block1006. Each of the messages may be defined (or segmented into) as a burstsegment or a reflection segment according to the analyzing andrecording, as in block 1008. One or more topic modelling operations maybe enhanced (and/or developed) for text segmentation using the burstsegment(s) or reflection segment(s), as in block 1010. The functionality1000 may end in block 1012.

In one aspect, in conjunction with and/or as part of at least one blockof FIG. 10, the operation of 1000 may include one or more of each of thefollowing. The operation of 1000 may analyze the burst segments orreflection segments. The burst segments are messages within a selectedtime period and the reflection segments are messages occurring after aselected time period. The burst segments and/or reflection segments maybe used to determine optimal topic model sizes. The operation of 1000may use the one or more enhanced topic models (e.g., a topic modeloperation using the first group and the second group) for selecting atopic cluster size, selecting a topic term size, determining apercentage of unique stop words, and/or altering a topic cluster sizeand a topic term size until a percentage of unique stop words aremaximized.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowcharts and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowcharts and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowcharts and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks that may be shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts or carry out combinations of special purposehardware and computer instructions.

The invention claimed is:
 1. A method for text segmentation for topicmodelling by a processor, comprising: analyzing real-time conversationdata, wherein time intervals between messages being received into theconversation data are recorded; defining the messages as burst segmentsor reflection segments according to the analyzing; wherein the burstsegments comprise successive messages received into the conversationdata within a first time interval and the reflection segments comprisemultiple messages each received into the conversation data having aninter-arrival time outside the first time interval; enhancing, using amachine learning mechanism, one or more topic modelling operations fortext segmentation using the burst segments or reflection segments; andpresenting, via a display, a summary of the one or more topic modellingoperations to a user according to an output of a text mining analysisimplementing the one or more topic modelling operations enhanced by themachine learning mechanism.
 2. The method of claim 1, further includingusing the burst segments or reflection segments to determine optimaltopic model sizes.
 3. The method of claim 1, further including selectinga topic cluster size using the one or more enhanced topic modellingoperations.
 4. The method of claim 1, further including selecting atopic term size using the one or more enhanced topic modellingoperations.
 5. The method of claim 1, further including determining apercentage of unique stop words.
 6. The method of claim 1, furtherincluding altering a topic cluster size and a topic term size until apercentage of unique stop words are maximized.
 7. A system for textsegmentation for topic modelling in a computing environment, comprising:one or more computers with executable instructions that when executedcause the system to: analyze real-time conversation data, wherein timeintervals between messages being received into the conversation data arerecorded; define the messages as burst segments or reflection segmentsaccording to the analyzing; wherein the burst segments comprisesuccessive messages received into the conversation data within a firsttime interval and the reflection segments comprise multiple messageseach received into the conversation data having an inter-arrival timeoutside the first time interval; enhance, using a machine learningmechanism, one or more topic modelling operations for text segmentationusing the burst segments or reflection segments; and present, via adisplay, a summary of the one or more topic modelling operations to auser according to an output of a text mining analysis implementing theone or more topic modelling operations enhanced by the machine learningmechanism.
 8. The system of claim 7, wherein the executable instructionswhen executed cause the system to use the burst segments or reflectionsegments to determine optimal topic model sizes.
 9. The system of claim7, wherein the executable instructions when executed cause the system toselect a topic cluster size using the one or more enhanced topicmodelling operations.
 10. The system of claim 7, wherein the executableinstructions when executed cause the system to select a topic term sizeusing the one or more enhanced topic modelling operations.
 11. Thesystem of claim 7, wherein the executable instructions when executedcause the system to determine a percentage of unique stop words.
 12. Thesystem of claim 7, wherein the executable instructions when executedcause the system to alter a topic cluster size and a topic term sizeuntil a percentage of unique stop words are maximized.
 13. A computerprogram product for, by a processor, text segmentation for topicmodelling, the computer program product comprising a non-transitorycomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionscomprising: an executable portion that analyzes real-time conversationdata, wherein time intervals between messages being received into theconversation data are recorded; an executable portion that defines themessages as burst segments or reflection segments according to theanalyzing; wherein the burst segments comprise successive messagesreceived into the conversation data within a first time interval and thereflection segments comprise multiple messages each received into theconversation data having an inter-arrival time outside the first timeinterval; an executable portion that enhances, using a machine learningmechanism, one or more topic modelling operations for text segmentationusing the burst segments or reflection segments; and an executableportion that presents, via a display, a summary of the one or more topicmodelling operations to a user according to an output of a text mininganalysis implementing the one or more topic modelling operationsenhanced by the machine learning mechanism.
 14. The computer programproduct of claim 13, further including an executable portion that usesthe burst segments or reflection segments to determine optimal topicmodel sizes.
 15. The computer program product of claim 13, furtherincluding an executable portion that selects a topic cluster size usingthe one or more enhanced topic modelling operations.
 16. The computerprogram product of claim 13, further including an executable portionthat: selects a topic term size; and determine a percentage of uniquestop words.
 17. The computer program product of claim 13, furtherincluding an executable portion that alters a topic cluster size and atopic term size until a percentage of unique stop words are maximized.